Example usage for org.apache.mahout.classifier OnlineLearner train

Introduction

In this page you can find the example usage for org.apache.mahout.classifier OnlineLearner train.

Prototype

void train(int actual, Vector instance);

Source Link

Document

Updates the model using a particular target variable value and a feature vector.

Usage

From source file:gov.llnl.ontology.mains.ExtendWordNet.java

License:Open Source License

/**
 * Trains the {@code hypernymPredictor} using the evidence from an {@link
 * EvidenceMap} and the class labels for each data point in that map.
 * //from w  w  w  .ja  v a 2s.co  m
 * @param map The raw {@link Map} holding {@link Evidence} instances
 * @param model The model to be trained
 * @param classLabel the class label to be applied to all data in {@code
 *        map}
 */
private void trainHypernyms(Map<String, Map<String, Evidence>> map, OnlineLearner model, int classLabel) {
    for (Map<String, Evidence> value : map.values())
        for (Evidence e : value.values())
            model.train(classLabel, new MahoutSparseVector(e.vector, basis.numDimensions()));
}

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From source file:gov.llnl.ontology.mains.ExtendWordNet.java

License:Open Source License

/**
 * Trains the {@code cousinPredictor} using the evidence from an {@link
 * EvidenceMap} and the similarity scores for each data point in that map.
 * // w ww .ja  v a2 s.c o  m
 * @param map The raw {@link Map} holding {@link Evidence} instances
 * @param model The model to be trained
 */
private void trainCousins(Map<String, Map<String, Evidence>> map, OnlineLearner model) {
    for (Map.Entry<String, Map<String, Evidence>> entry : map.entrySet()) {
        for (Map.Entry<String, Evidence> ev : entry.getValue().entrySet()) {
            // Determine whether or not the two terms are cousins.
            // Initially assume that they arent.
            Pair<Integer> cousinDepth = SynsetRelations.getCousinDistance(
                    reader.getSynsets(entry.getKey(), PartsOfSpeech.NOUN),
                    reader.getSynsets(ev.getKey(), PartsOfSpeech.NOUN), 7);

            // If the two terms are cousins, use 1 as the class label,
            // otherwise use 0.
            int classLabel = 0;
            if (cousinDepth.x != Integer.MAX_VALUE && cousinDepth.y != Integer.MAX_VALUE)
                classLabel = 1;

            // Train the model with this data point.
            model.train(classLabel, new DenseVector(ev.getValue().similarityScores));
        }
    }
}

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From source file:opennlp.addons.mahout.AbstractOnlineLearnerTrainer.java

License:Apache License

protected void trainOnlineLearner(DataIndexer indexer, org.apache.mahout.classifier.OnlineLearner pa) {
    int cardinality = indexer.getPredLabels().length;
    int outcomes[] = indexer.getOutcomeList();

    for (int i = 0; i < indexer.getContexts().length; i++) {

        Vector vector = new RandomAccessSparseVector(cardinality);

        int features[] = indexer.getContexts()[i];

        for (int fi = 0; fi < features.length; fi++) {
            vector.set(features[fi], indexer.getNumTimesEventsSeen()[i]);
        }//from  w  ww. j av a2s.  co m

        pa.train(outcomes[i], vector);
    }
}

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